We focus on the random generation of SAT instances that have properties similar to real-world instances. It is known that many industrial instances, even with a great number of variables, can be solved by a clever solver in a reasonable amount of time. This is not possible, in general, with classical randomly generated instances. We provide a different generation model of SAT instances, called \emph{scale-free random SAT instances}. It is based on the use of a non-uniform probability distribution $P(i)\sim i^{-β}$ to select variable $i$, where $β$ is a parameter of the model. This results into formulas where the number of occurrences $k$ of variables follows a power-law distribution $P(k)\sim k^{-δ}$ where $δ= 1 + 1/β$. This property has been observed in most real-world SAT instances. For $β=0$, our model extends classical random SAT instances. We prove the existence of a SAT-UNSAT phase transition phenomenon for scale-free random 2-SAT instances with $β<1/2$ when the clause/variable ratio is $m/n=\frac{1-2β}{(1-β)^2}$. We also prove that scale-free random k-SAT instances are unsatisfiable with high probability when the number of clauses exceeds $ω(n^{(1-β)k})$. %This implies that the SAT/UNSAT phase transition phenomena vanishes when $β>1-1/k$, and formulas are unsatisfiable due to a small core of clauses. The proof of this result suggests that, when $β>1-1/k$, the unsatisfiability of most formulas may be due to small cores of clauses. Finally, we show how this model will allow us to generate random instances similar to industrial instances, of interest for testing purposes.